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Develop and oversee a comprehensive data architecture, aligning with business goals and integrating technologies such as Azure, Databricks, and Palantir to craft a forward-looking data management and analytics landscape.
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Lead the design of enterprise-grade data platforms addressing needs across Data Engineering, Data Science, and Data Analysis, capitalizing on the capabilities of Azure Databricks. In the role of Senior Data Architect, you will be instrumental in architecting, implementing, and optimizing our data processing and analytics platform based on Databricks for our customer facing Trinity Logistics Platform (TLP.
Full-timeExpandApply NowActive JobUpdated 55 days ago - UpvoteDownvoteShare Job
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This Principal Data Engineer Consultant will play the role of a data and AI solution architect, primarily focusing on presales and leading client engagements related to data warehousing, ETL development, data integrations, and data modeling (databricks and the 3 clouds.
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The ideal candidate will have extensive Microsoft/Azure data services and Databricks technology experience. Design, develop and maintain data pipelines using Databricks and Microsoft Azure services.
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The ideal candidate will have a strong background in utilizing Databricks for data exploration and analysis, alongside working knowledge of Snowflake and dbt. Familiarity with cloud platforms, particularly Azure services such as Azure Machine Learning, Azure Databricks, Azure Data Factory, or Azure Synapse Analytics.
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We provideexpertise in Azure, AWS, GCP, Snowflake, Databricks, and Python. For our sustainable growth, we are seeking for a Databricks Technical Lead who can elevate our Databricks competence center to the next level.
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The ideal candidate will have a minimum of 8-10 years of relevant experience, with a focus on Azure Databricks environment, ML Ops, ML model drift monitoring, Databricks Feature Store, and proficiency in utilizing tools like Jira and Confluence.
$165,000 - $170,000 a yearFull-timeExpandApply NowActive JobUpdated 1 days ago - UpvoteDownvoteShare Job
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Direct experience of building data pipelines using Azure Data Factory and Apache Spark (preferably in Databricks). Job Title:- Databricks Azure Engineer. Designing and implementing highly performant data pipelines from multiple sources using Apache Spark and/or Azure Databricks.
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Experience with data movement and transformation capabilities (Azure Data Factory, Data Lake Analytics, Databricks, Stream Analytics, and Azure Functions). Strong development background creating data pipelines and complex data transformations using one of the languages like Python, Databricks/Spark, T-SQL.
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The job requires expertise in Databricks, a cloud-based data engineering. Work closely with Engineering and Product Management to drive the direction of the Databricks product. Contribute to Databricks SME community.
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Proficient in data engineering roles with a focus on AWS and Databricks. Utilize Databricks for big data processing, analytics, and machine learning workflows. Optimize data processing and query performance for large-scale datasets within AWS and Databricks environments.
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You will be utilizing Snowflake and/or Databricks tools, cutting-edge DevOps technologies, developing advanced analytics products, and applying data and statistical programming tools to enterprise data to advance and enable key mission outcomes within cloud environments (GCP, AWS, Azure.
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Hands-on experience in implementing Data Lake/Data Warehouse with technologies like – Databricks, Azure Synapse Analytics, SQL Database, AWS Lake formation. Design, Architect, Deploy, and maintain solutions on AWS and Databricks to provide secure and governed access to data for data scientist, data analysts and business users.
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More than 10,000 organizations worldwide — including Comcast, Condé Nast, Grammarly, and over 50% of the Fortune 500 — rely on the Databricks Data Intelligence Platform to unify and democratize data, analytics and AI. Databricks is headquartered in San Francisco, with offices around the globe and was founded by the original creators of Lakehouse, Apache Spark, Delta Lake and MLflow.
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Customers rely on Databricks for the full range of data workloads, including data engineering, ETL, near real-time streaming, machine learning, SQL analytics, and advanced analytics. Deliver thought leadership and best practices in Data Warehousing and in particular in migrating data warehousing workloads from legacy tools to the Databricks Data Intelligence Platform, ranging from architectural and security design to development and deployment approaches.
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